BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Abstract
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. BoTorch's modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. We also propose a novel "one-shot" formulation of the Knowledge Gradient, enabled by a combination of our theoretical and software contributions. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries.
Cite
@article{arxiv.1910.06403,
title = {BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization},
author = {Maximilian Balandat and Brian Karrer and Daniel R. Jiang and Samuel Daulton and Benjamin Letham and Andrew Gordon Wilson and Eytan Bakshy},
journal= {arXiv preprint arXiv:1910.06403},
year = {2020}
}